Neural Machine Translation (NMT): Deep learning approaches through Neural Network Models

Junhui Hu

doi:10.54254/2755-2721/82/20240944

Research Article

Open access

Published on 8 November 2024

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Hu,J. (2024). Neural Machine Translation (NMT): Deep learning approaches through Neural Network Models. Applied and Computational Engineering,82,93-99.

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Neural Machine Translation (NMT): Deep learning approaches through Neural Network Models

Junhui Hu *^,1,

¹ Shandong University, Shandong, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/82/20240944

Abstract

This paper explores the significant advancements in Neural Machine Translation (NMT) models, focusing on the impact of different architectures, training methodologies, and optimization techniques on translation quality. The study contrasts the performance of Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and the Transformer model, highlighting the superior capabilities of the Transformer in handling long-range dependencies and providing contextually accurate translations. Key optimization techniques, such as learning rate scheduling, dropout regularization, and gradient clipping, are discussed in detail, emphasizing their roles in enhancing model performance and training efficiency. Furthermore, the paper presents a comparative analysis of NMT and traditional Statistical Machine Translation (SMT) systems, showcasing NMT's superior BLEU scores and fluency. The application of model distillation is also examined, demonstrating how smaller models can achieve high performance with reduced computational resources. These findings underscore the transformative potential of NMT in achieving state-of-the-art translation quality and efficiency.

Keywords

Neural Machine Translation, Transformer Model, Recurrent Neural Networks, Convolutional Neural Networks.

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References

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[4]. Fernandes, Patrick, et al. "Scaling laws for multilingual neural machine translation." International Conference on Machine Learning. PMLR, 2023.

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[6]. Reheman, Abudurexiti, et al. "Prompting neural machine translation with translation memories." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 37. No. 11. 2023.

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Cite this article

Hu,J. (2024). Neural Machine Translation (NMT): Deep learning approaches through Neural Network Models. Applied and Computational Engineering,82,93-99.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation

Conference website: https://2024.confmla.org/

ISBN：978-1-83558-565-8(Print) / 978-1-83558-566-5(Online)

Conference date: 21 November 2024

Editor：Mustafa ISTANBULLU, Anil Fernando

Series: Applied and Computational Engineering

Volume number: Vol.82

ISSN：2755-2721(Print) / 2755-273X(Online)

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